CAJ | 학술논문

针对频谱分析不能确定轴承故障程度的缺点，提出 PCA（principle component analysis）与优化的 SVM （support vector machine）相结合的方法，研究轴承不同故障类型、同种故障类型不同故障等级的实验振动数据，同时对轴承振动信号的13个特征属性参数进行了主成分分析，确定了最优特征属性参数，并利用优化的SVM 对轴承故障进行诊断。实验结果表明：该方法确定了最优属性参数，减少了冗余信息，提高了诊断准确率，减少了时间消耗，不仅有效地诊断出了轴承的故障类别，而且实现了轴承的故障等级诊断，使诊断更加精细化，为工程实际中轴承的健康管理提供了有益参考。
침대빈보분석불능학정축승고장정도적결점，제출 PCA（principle component analysis）여우화적 SVM （support vector machine）상결합적방법，연구축승불동고장류형、동충고장류형불동고장등급적실험진동수거，동시대축승진동신호적13개특정속성삼수진행료주성분분석，학정료최우특정속성삼수，병이용우화적SVM 대축승고장진행진단。실험결과표명：해방법학정료최우속성삼수，감소료용여신식，제고료진단준학솔，감소료시간소모，불부유효지진단출료축승적고장유별，이차실현료축승적고장등급진단，사진단경가정세화，위공정실제중축승적건강관리제공료유익삼고。
As the conventional frequency spectrum analysis method fails to determine the fault levels of bearing,this paper proposes a method combining PCA (principle component analysis)with the im-proved SVM (support vector machine)to diagnose fault levels of bearing.According to the bearing vibration experiment data of different levels of fault types,13 characteristics parameters of bearing vi-bration signals are extracted as the training and diagnosis samples to determine the bearing states. This method can determine the extracted features of the properties parameters,thereby reducing the redundant information and improving the diagnostic accuracy in the process of diagnosis.Experimental research shows that the improved method can not only diagnose the bearing fault category effectively but also identify the fault levels of bearing,which provides a useful guide for precision diagnosis of bearing malfunction.